import argparse
import os
import random
import shutil
import time
import warnings
from enum import Enum
import deepspeed

from deepspeed.runtime.data_pipeline.data_routing.helper import convert_to_random_ltd, save_without_random_ltd

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
from torch.optim.lr_scheduler import StepLR
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
from torch.utils.data import Subset
import models
from models.vit import Block
import math

from timm.data.transforms_factory import  transforms_imagenet_eval, transforms_imagenet_train
model_names = sorted(name for name in models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(models.__dict__[name]))

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR', nargs='?', default='imagenet',
                    help='path to dataset (default: imagenet)')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
                    metavar='N',
                    help='mini-batch size (default: 256), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
                    metavar='N', help='print  frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--world-size', default=-1, type=int,
                    help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
                    help='node rank for distributed training')
parser.add_argument('--local_rank', default=-1, type=int,
                    help='local rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
                    help='GPU id to use.')
parser.add_argument('--img_size', default=224, type=int,
                    help='image size ')
parser.add_argument('--out_dir', default=None, type=str,
                    help='output_dir.')
parser.add_argument('--scheduler', default="cosine", type=str,
                    help='lr scheduler')
parser.add_argument("--warmup", action="store_true", help="use token scheduler or not.")
parser.add_argument('--multiprocessing-distributed', action='store_true',
                    help='Use multi-processing distributed training to launch '
                         'N processes per node, which has N GPUs. This is the '
                         'fastest way to use PyTorch for either single node or '
                         'multi node data parallel training')
parser.add_argument('--dummy', action='store_true', help="use fake data to benchmark")
parser.add_argument('--random_ltd', action='store_true', help="use fake data to benchmark")
parser = deepspeed.add_config_arguments(parser)
best_acc1 = 0
history = {"train_loss": [], "train_acc1": [],"train_acc5":[], "val_loss": [], "val_acc1": [], "val_acc5": [], "epoch": [0,] }

def _get_model(args):
    # create model
    print ("=> creating model '{}'".format(args.arch))
    nclasses = 1000
    nchannels = 3
    model = models.__dict__[args.arch](num_classes=nclasses, nchannels=nchannels)
    return model
    
def _get_dist_model(gpu, args):
    ngpus_per_node = torch.cuida.device_count()
    if args.distributed:
        if args.dist_url == "env://" and args.rank == -1:
            args.rank = int(os.environ["RANK"])
        if args.multiprocessing_distributed:
            # For multiprocessing distributed training, rank needs to be the
            # global rank among all the processes
            args.rank = args.rank * ngpus_per_node + gpu
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size, rank=args.rank)
    model = _get_model(args)
    if not torch.cuda.is_available():
        print ('using CPU, this will be slow')
    elif args.distributed:
        # For multiprocessing distributed, DistributedDataParallel constructor
        # should always set the single device scope, otherwise,
        # DistributedDataParallel will use all available devices.
        if args.gpu is not None:
            torch.cuda.set_device(args.gpu)
            model.cuda(args.gpu)
            # When using a single GPU per process and per
            # DistributedDataParallel, we need to divide the batch size
            # ourselves based on the total number of GPUs of the current node.
            args.batch_size = int(args.batch_size / ngpus_per_node)
            args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
            model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        else:
            model.cuda()
            # DistributedDataParallel will divide and allocate batch_size to all
            # available GPUs if device_ids are not set
            model = torch.nn.parallel.DistributedDataParallel(model)
    elif args.gpu is not None:
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
    else:
        # DataParallel will divide and allocate batch_size to all available GPUs
        if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
            model.features = torch.nn.DataParallel(model.features)
            model.cuda()
        else:
            model = torch.nn.DataParallel(model).cuda()
    return model
    
def main():
   
    args = parser.parse_args()

    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        #torch.cuda.manual_seed_all(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    if args.dist_url == "env://" and args.world_size == -1:
        args.world_size = int(os.environ["WORLD_SIZE"])

    args.distributed = args.world_size > 1 or args.multiprocessing_distributed or args.deepspeed

    ngpus_per_node = torch.cuda.device_count()
    if args.multiprocessing_distributed:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = ngpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
    else:
        # Simply call main_worker function
        main_worker(args.gpu, ngpus_per_node, args)


def main_worker(gpu, ngpus_per_node, args):
    global best_acc1
    global history
    
    if args.deepspeed:
        gpu = args.local_rank
    args.gpu = gpu
    ngpus = torch.cuda.device_count()
    if args.gpu is not None:
        print ("Use GPU: {} for training".format(args.gpu))

    if not args.deepspeed:
        model = _get_dist_model(gpu, args)
    else:
        model = _get_model(args)
        deepspeed.init_distributed()
    print(f'created model on gpu {gpu}')
    # exit ()
    
    # define loss function (criterion), optimizer, and learning rate scheduler
    criterion = nn.CrossEntropyLoss().cuda(args.gpu)


    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print ("=> loading checkpoint '{}'".format(args.resume))
            if args.gpu is None:
                checkpoint = torch.load(args.resume)
            else:
                # Map model to be loaded to specified single gpu.
                loc = 'cuda:{}'.format(args.gpu)
                checkpoint = torch.load(args.resume, map_location=loc)
            args.start_epoch = checkpoint['epoch']
            best_acc1 = checkpoint['best_acc1']
            # if args.gpu is not None:
            #     # best_acc1 may be from a checkpoint from a different GPU
            #     best_acc1 = best_acc1.to(args.gpu)\
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            #scheduler.load_state_dict(checkpoint['scheduler'])
            print ("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print ("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    # Data loading code
    if args.dummy:
        print ("=> Dummy data is used!")
        train_dataset = datasets.FakeData(1281167, (3, args.img_size, args.img_size), 1000, transforms.ToTensor())
        val_dataset = datasets.FakeData(50000, (3, args.img_size, args.img_size), 1000, transforms.ToTensor())
    else:
        traindir = os.path.join(args.data, 'train')
        valdir = os.path.join(args.data, 'val')
        normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
        #transform_train = transforms_imagenet_eval


        val_dataset = datasets.ImageFolder(
            valdir,
            transforms.Compose([
                # transforms.Resize(256),
                # transforms.CenterCrop(224),
                transforms.Resize(args.img_size),
                transforms.CenterCrop(args.img_size),
                transforms.ToTensor(),
                normalize,
            ]))
        #train_dataset =  val_dataset
        train_dataset = datasets.ImageFolder(
            traindir,
            transforms.Compose([
                transforms.Resize(args.img_size),
                transforms.RandomCrop(args.img_size, padding=(args.img_size//8)),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ]))
    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
        val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, drop_last=True)
    else:
        train_sampler = None
        val_sampler = None
    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True, sampler=val_sampler)
    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler)

    if args.evaluate:
        validate(val_loader, model, criterion, args)
        # return
    args.completed_step = 0
    
    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)
    
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    scheduler = StepLR(optimizer, step_size=int(len(train_loader)*args.epochs//3), gamma=0.1)# None #
    

    model, optimizer, _, scheduler  = deepspeed.initialize(
        model=model,
        optimizer=optimizer,
        lr_scheduler=scheduler,
        args=args,
        dist_init_required=False)
    if args.random_ltd:
        model = convert_to_random_ltd(model, Block)
    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)

        # train for one epoch
        start_time = time.time()
        top5_train, top1_train, losses_train, args = train(scheduler, train_loader, model, criterion, optimizer, epoch, args)
        time_epoch = time.time() - start_time
        # evaluate on validation set
        top5_val, top1_val, losses_val = validate(val_loader, model, criterion, args)
        if args.gpu==0: 
            history["epoch"].append(epoch)
            history["val_loss"].append(losses_val)
            history["val_acc1"].append(top1_val) 
            history["val_acc5"].append(top5_val) 
            history["train_loss"].append(losses_train)
            history["train_acc1"].append(top1_train) 
            history["train_acc5"].append(top5_train)
            torch.save(history,f"{args.out_dir}/stat.pt") 
            try:
                print (f'{epoch} epoch at time {time_epoch}s and learning rate {scheduler.get_last_lr()}') 
            except:
                print (f'{epoch} epoch at time {time_epoch}s and learning rate {args.lr}')
            print (f"finish epoch {epoch} or iteration {args.completed_step}, train_accuracy is {top1_train}, val_accuracy {top1_val}")

        # remember best acc@1 and save checkpoint
        is_best = top1_val > best_acc1
        best_acc1 = max(top1_val, best_acc1)
        if is_best:
            if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                    and args.rank % ngpus_per_node == 0) or args.local_rank==0:
                save_checkpoint({
                    'epoch': epoch + 1,
                    'arch': args.arch,
                    'state_dict': save_without_random_ltd(model),
                    'best_acc1': best_acc1,
                    'optimizer' : optimizer.state_dict()
                }, is_best, filename=f"{args.out_dir}/checkpoint.pth.tar")


def train(scheduler, train_loader, model, criterion, optimizer, epoch, args):
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    length = AverageMeter('Reserve_length', ':6.0f')
    lr_progress = AverageMeter('lr', ':0f')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5, lr_progress,length,],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()

    end = time.time()
    for i, (images, target) in enumerate(train_loader):
        args.completed_step += 1
        # measure data loading time
        try:
            reserved_length =  model.random_ltd_scheduler.get_current_seq()
        except:
            reserved_length =  -1
        data_time.update(time.time() - end)

        if args.gpu is not None:
            images = images.cuda(args.gpu, non_blocking=True)
        if torch.cuda.is_available():
            target = target.cuda(args.gpu, non_blocking=True)

        # compute output
        output = model(images)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.clone().detach(), images.size(0))
        top1.update(acc1[0], images.size(0))
        top5.update(acc5[0], images.size(0))

        length.update(reserved_length, 1)
        lr_progress.update(scheduler.get_last_lr()[0], 1)
        # compute gradient and do SGD step
        if args.deepspeed:
            model.backward(loss)
            model.step()
        else:
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            scheduler.step()
            
        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0 and args.gpu==0: 
            progress.display(i + 1)
            
    if args.distributed:
        losses.all_reduce()
        top1.all_reduce()
        top5.all_reduce()

    return top5.avg, top1.avg, losses.avg, args

def validate(val_loader, model, criterion, args):
    def run_validate(loader, base_progress=0):
        with torch.no_grad():
            end = time.time()
            for i, (images, target) in enumerate(loader):
                i = base_progress + i
                if args.gpu is not None:
                    images = images.cuda(args.gpu, non_blocking=True)
                if torch.cuda.is_available():
                    target = target.cuda(args.gpu, non_blocking=True)

                # compute output
                output = model(images)
                loss = criterion(output, target)
                # measure accuracy and record loss
                acc1, acc5 = accuracy(output, target, topk=(1, 5))
                losses.update(loss.clone(), images.size(0))
                top1.update(acc1[0], images.size(0))
                top5.update(acc5[0], images.size(0))

                # measure elapsed time
                batch_time.update(time.time() - end)
                end = time.time()

                if i % args.print_freq == 0 and args.gpu==0: 
                    progress.display(i + 1)

    batch_time = AverageMeter('Time', ':6.3f', Summary.NONE)
    losses = AverageMeter('Loss', ':.4e', Summary.NONE)
    top1 = AverageMeter('Acc@1', ':6.2f', Summary.AVERAGE)
    top5 = AverageMeter('Acc@5', ':6.2f', Summary.AVERAGE)
    progress = ProgressMeter(
        len(val_loader) + (args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset))),
        [batch_time, losses, top1, top5],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    run_validate(val_loader)
    if args.distributed:
        top1.all_reduce()
        top5.all_reduce()
        losses.all_reduce()

    if args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset)):
        aux_val_dataset = Subset(val_loader.dataset,
                                 range(len(val_loader.sampler) * args.world_size, len(val_loader.dataset)))
        aux_val_loader = torch.utils.data.DataLoader(
            aux_val_dataset, batch_size=args.batch_size, shuffle=False,
            num_workers=args.workers, pin_memory=True)
        run_validate(aux_val_loader, len(val_loader))

    progress.display_summary()

    return top5.avg, top1.avg, losses.avg


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, 'model_best.pth.tar')

class Summary(Enum):
    NONE = 0
    AVERAGE = 1
    SUM = 2
    COUNT = 3

class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f', summary_type=Summary.AVERAGE):
        self.name = name
        self.fmt = fmt
        self.summary_type = summary_type
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def all_reduce(self):
        #total = torch.FloatTensor([self.sum, self.count])
        #total = torch.Tensor([self.sum, self.count], device=self.sum.device, dtype=torch.float32)
        total = torch.FloatTensor([self.sum, self.count]).cuda(self.sum.device)

        dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False)
        self.sum, self.count = total.tolist()
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)
    
    def summary(self):
        fmtstr = ''
        if self.summary_type is Summary.NONE:
            fmtstr = ''
        elif self.summary_type is Summary.AVERAGE:
            fmtstr = '{name} {avg:.3f}'
        elif self.summary_type is Summary.SUM:
            fmtstr = '{name} {sum:.3f}'
        elif self.summary_type is Summary.COUNT:
            fmtstr = '{name} {count:.3f}'
        else:
            raise ValueError('invalid summary type %r' % self.summary_type)
        
        return fmtstr.format(**self.__dict__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print ('\t'.join(entries))
        
    def display_summary(self):
        entries = [" *"]
        entries += [meter.summary() for meter in self.meters]
        print (' '.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'

def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


if __name__ == '__main__':
    main()
